/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    CostSensitiveClassifier.java
 *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.meta;

import weka.classifiers.Classifier;
import weka.classifiers.CostMatrix;
import weka.classifiers.RandomizableSingleClassifierEnhancer;
import weka.core.Capabilities;
import weka.core.Drawable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.StringReader;
import java.io.StringWriter;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> A metaclassifier that makes its base classifier
 * cost-sensitive. Two methods can be used to introduce cost-sensitivity:
 * reweighting training instances according to the total cost assigned to each
 * class; or predicting the class with minimum expected misclassification cost
 * (rather than the most likely class). Performance can often be improved by
 * using a Bagged classifier to improve the probability estimates of the base
 * classifier.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -M
 *  Minimize expected misclassification cost. Default is to
 *  reweight training instances according to costs per class
 * </pre>
 * 
 * <pre>
 * -C &lt;cost file name&gt;
 *  File name of a cost matrix to use. If this is not supplied,
 *  a cost matrix will be loaded on demand. The name of the
 *  on-demand file is the relation name of the training data
 *  plus ".cost", and the path to the on-demand file is
 *  specified with the -N option.
 * </pre>
 * 
 * <pre>
 * -N &lt;directory&gt;
 *  Name of a directory to search for cost files when loading
 *  costs on demand (default current directory).
 * </pre>
 * 
 * <pre>
 * -cost-matrix &lt;matrix&gt;
 *  The cost matrix in Matlab single line format.
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <pre>
 * -W
 *  Full name of base classifier.
 *  (default: weka.classifiers.rules.ZeroR)
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * Options after -- are passed to the designated classifier.
 * <p>
 * 
 * @author Len Trigg (len@reeltwo.com)
 * @version $Revision: 1.29 $
 */
public class CostSensitiveClassifier extends
		RandomizableSingleClassifierEnhancer implements OptionHandler, Drawable {

	/** for serialization */
	static final long serialVersionUID = -720658209263002404L;

	/** load cost matrix on demand */
	public static final int MATRIX_ON_DEMAND = 1;
	/** use explicit cost matrix */
	public static final int MATRIX_SUPPLIED = 2;
	/** Specify possible sources of the cost matrix */
	public static final Tag[] TAGS_MATRIX_SOURCE = {
			new Tag(MATRIX_ON_DEMAND, "Load cost matrix on demand"),
			new Tag(MATRIX_SUPPLIED, "Use explicit cost matrix") };

	/** Indicates the current cost matrix source */
	protected int m_MatrixSource = MATRIX_ON_DEMAND;

	/**
	 * The directory used when loading cost files on demand, null indicates
	 * current directory
	 */
	protected File m_OnDemandDirectory = new File(
			System.getProperty("user.dir"));

	/** The name of the cost file, for command line options */
	protected String m_CostFile;

	/** The cost matrix */
	protected CostMatrix m_CostMatrix = new CostMatrix(1);

	/**
	 * True if the costs should be used by selecting the minimum expected cost
	 * (false means weight training data by the costs)
	 */
	protected boolean m_MinimizeExpectedCost;

	/**
	 * String describing default classifier.
	 * 
	 * @return the default classifier classname
	 */
	protected String defaultClassifierString() {

		return "weka.classifiers.rules.ZeroR";
	}

	/**
	 * Default constructor.
	 */
	public CostSensitiveClassifier() {
		m_Classifier = new weka.classifiers.rules.ZeroR();
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector newVector = new Vector(5);

		newVector
				.addElement(new Option(
						"\tMinimize expected misclassification cost. Default is to\n"
								+ "\treweight training instances according to costs per class",
						"M", 0, "-M"));
		newVector
				.addElement(new Option(
						"\tFile name of a cost matrix to use. If this is not supplied,\n"
								+ "\ta cost matrix will be loaded on demand. The name of the\n"
								+ "\ton-demand file is the relation name of the training data\n"
								+ "\tplus \".cost\", and the path to the on-demand file is\n"
								+ "\tspecified with the -N option.", "C", 1,
						"-C <cost file name>"));
		newVector.addElement(new Option(
				"\tName of a directory to search for cost files when loading\n"
						+ "\tcosts on demand (default current directory).",
				"N", 1, "-N <directory>"));
		newVector.addElement(new Option(
				"\tThe cost matrix in Matlab single line format.",
				"cost-matrix", 1, "-cost-matrix <matrix>"));

		Enumeration enu = super.listOptions();
		while (enu.hasMoreElements()) {
			newVector.addElement(enu.nextElement());
		}

		return newVector.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -M
	 *  Minimize expected misclassification cost. Default is to
	 *  reweight training instances according to costs per class
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;cost file name&gt;
	 *  File name of a cost matrix to use. If this is not supplied,
	 *  a cost matrix will be loaded on demand. The name of the
	 *  on-demand file is the relation name of the training data
	 *  plus ".cost", and the path to the on-demand file is
	 *  specified with the -N option.
	 * </pre>
	 * 
	 * <pre>
	 * -N &lt;directory&gt;
	 *  Name of a directory to search for cost files when loading
	 *  costs on demand (default current directory).
	 * </pre>
	 * 
	 * <pre>
	 * -cost-matrix &lt;matrix&gt;
	 *  The cost matrix in Matlab single line format.
	 * </pre>
	 * 
	 * <pre>
	 * -S &lt;num&gt;
	 *  Random number seed.
	 *  (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <pre>
	 * -W
	 *  Full name of base classifier.
	 *  (default: weka.classifiers.rules.ZeroR)
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.rules.ZeroR:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * Options after -- are passed to the designated classifier.
	 * <p>
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		setMinimizeExpectedCost(Utils.getFlag('M', options));

		String costFile = Utils.getOption('C', options);
		if (costFile.length() != 0) {
			try {
				setCostMatrix(new CostMatrix(new BufferedReader(new FileReader(
						costFile))));
			} catch (Exception ex) {
				// now flag as possible old format cost matrix. Delay cost
				// matrix
				// loading until buildClassifer is called
				setCostMatrix(null);
			}
			setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
					TAGS_MATRIX_SOURCE));
			m_CostFile = costFile;
		} else {
			setCostMatrixSource(new SelectedTag(MATRIX_ON_DEMAND,
					TAGS_MATRIX_SOURCE));
		}

		String demandDir = Utils.getOption('N', options);
		if (demandDir.length() != 0) {
			setOnDemandDirectory(new File(demandDir));
		}

		String cost_matrix = Utils.getOption("cost-matrix", options);
		if (cost_matrix.length() != 0) {
			StringWriter writer = new StringWriter();
			CostMatrix.parseMatlab(cost_matrix).write(writer);
			setCostMatrix(new CostMatrix(new StringReader(writer.toString())));
			setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED,
					TAGS_MATRIX_SOURCE));
		}

		super.setOptions(options);
	}

	/**
	 * Gets the current settings of the Classifier.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		String[] superOptions = super.getOptions();
		String[] options = new String[superOptions.length + 7];

		int current = 0;

		if (m_MatrixSource == MATRIX_SUPPLIED) {
			if (m_CostFile != null) {
				options[current++] = "-C";
				options[current++] = "" + m_CostFile;
			} else {
				options[current++] = "-cost-matrix";
				options[current++] = getCostMatrix().toMatlab();
			}
		} else {
			options[current++] = "-N";
			options[current++] = "" + getOnDemandDirectory();
		}

		if (getMinimizeExpectedCost()) {
			options[current++] = "-M";
		}

		System.arraycopy(superOptions, 0, options, current, superOptions.length);

		while (current < options.length) {
			if (options[current] == null) {
				options[current] = "";
			}
			current++;
		}

		return options;
	}

	/**
	 * @return a description of the classifier suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String globalInfo() {

		return "A metaclassifier that makes its base classifier cost-sensitive. "
				+ "Two methods can be used to introduce cost-sensitivity: reweighting "
				+ "training instances according to the total cost assigned to each "
				+ "class; or predicting the class with minimum expected "
				+ "misclassification cost (rather than the most likely class). "
				+ "Performance can often be "
				+ "improved by using a Bagged classifier to improve the probability "
				+ "estimates of the base classifier.";
	}

	/**
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String costMatrixSourceTipText() {

		return "Sets where to get the cost matrix. The two options are"
				+ "to use the supplied explicit cost matrix (the setting of the "
				+ "costMatrix property), or to load a cost matrix from a file when "
				+ "required (this file will be loaded from the directory set by the "
				+ "onDemandDirectory property and will be named relation_name"
				+ CostMatrix.FILE_EXTENSION + ").";
	}

	/**
	 * Gets the source location method of the cost matrix. Will be one of
	 * MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
	 * 
	 * @return the cost matrix source.
	 */
	public SelectedTag getCostMatrixSource() {

		return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE);
	}

	/**
	 * Sets the source location of the cost matrix. Values other than
	 * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
	 * 
	 * @param newMethod
	 *            the cost matrix location method.
	 */
	public void setCostMatrixSource(SelectedTag newMethod) {

		if (newMethod.getTags() == TAGS_MATRIX_SOURCE) {
			m_MatrixSource = newMethod.getSelectedTag().getID();
		}
	}

	/**
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String onDemandDirectoryTipText() {

		return "Sets the directory where cost files are loaded from. This option "
				+ "is used when the costMatrixSource is set to \"On Demand\".";
	}

	/**
	 * Returns the directory that will be searched for cost files when loading
	 * on demand.
	 * 
	 * @return The cost file search directory.
	 */
	public File getOnDemandDirectory() {

		return m_OnDemandDirectory;
	}

	/**
	 * Sets the directory that will be searched for cost files when loading on
	 * demand.
	 * 
	 * @param newDir
	 *            The cost file search directory.
	 */
	public void setOnDemandDirectory(File newDir) {

		if (newDir.isDirectory()) {
			m_OnDemandDirectory = newDir;
		} else {
			m_OnDemandDirectory = new File(newDir.getParent());
		}
		m_MatrixSource = MATRIX_ON_DEMAND;
	}

	/**
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String minimizeExpectedCostTipText() {

		return "Sets whether the minimum expected cost criteria will be used. If "
				+ "this is false, the training data will be reweighted according to the "
				+ "costs assigned to each class. If true, the minimum expected cost "
				+ "criteria will be used.";
	}

	/**
	 * Gets the value of MinimizeExpectedCost.
	 * 
	 * @return Value of MinimizeExpectedCost.
	 */
	public boolean getMinimizeExpectedCost() {

		return m_MinimizeExpectedCost;
	}

	/**
	 * Set the value of MinimizeExpectedCost.
	 * 
	 * @param newMinimizeExpectedCost
	 *            Value to assign to MinimizeExpectedCost.
	 */
	public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost) {

		m_MinimizeExpectedCost = newMinimizeExpectedCost;
	}

	/**
	 * Gets the classifier specification string, which contains the class name
	 * of the classifier and any options to the classifier
	 * 
	 * @return the classifier string.
	 */
	protected String getClassifierSpec() {

		Classifier c = getClassifier();
		if (c instanceof OptionHandler) {
			return c.getClass().getName() + " "
					+ Utils.joinOptions(((OptionHandler) c).getOptions());
		}
		return c.getClass().getName();
	}

	/**
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String costMatrixTipText() {
		return "Sets the cost matrix explicitly. This matrix is used if the "
				+ "costMatrixSource property is set to \"Supplied\".";
	}

	/**
	 * Gets the misclassification cost matrix.
	 * 
	 * @return the cost matrix
	 */
	public CostMatrix getCostMatrix() {

		return m_CostMatrix;
	}

	/**
	 * Sets the misclassification cost matrix.
	 * 
	 * @param newCostMatrix
	 *            the cost matrix
	 */
	public void setCostMatrix(CostMatrix newCostMatrix) {

		m_CostMatrix = newCostMatrix;
		m_MatrixSource = MATRIX_SUPPLIED;
	}

	/**
	 * Returns default capabilities of the classifier.
	 * 
	 * @return the capabilities of this classifier
	 */
	public Capabilities getCapabilities() {
		Capabilities result = super.getCapabilities();

		// class
		result.disableAllClasses();
		result.disableAllClassDependencies();
		result.enable(Capability.NOMINAL_CLASS);

		return result;
	}

	/**
	 * Builds the model of the base learner.
	 * 
	 * @param data
	 *            the training data
	 * @throws Exception
	 *             if the classifier could not be built successfully
	 */
	public void buildClassifier(Instances data) throws Exception {

		// can classifier handle the data?
		getCapabilities().testWithFail(data);

		// remove instances with missing class
		data = new Instances(data);
		data.deleteWithMissingClass();

		if (m_Classifier == null) {
			throw new Exception("No base classifier has been set!");
		}
		if (m_MatrixSource == MATRIX_ON_DEMAND) {
			String costName = data.relationName() + CostMatrix.FILE_EXTENSION;
			File costFile = new File(getOnDemandDirectory(), costName);
			if (!costFile.exists()) {
				throw new Exception("On-demand cost file doesn't exist: "
						+ costFile);
			}
			setCostMatrix(new CostMatrix(new BufferedReader(new FileReader(
					costFile))));
		} else if (m_CostMatrix == null) {
			// try loading an old format cost file
			m_CostMatrix = new CostMatrix(data.numClasses());
			m_CostMatrix.readOldFormat(new BufferedReader(new FileReader(
					m_CostFile)));
		}

		if (!m_MinimizeExpectedCost) {
			Random random = null;
			if (!(m_Classifier instanceof WeightedInstancesHandler)) {
				random = new Random(m_Seed);
			}
			data = m_CostMatrix.applyCostMatrix(data, random);
		}
		m_Classifier.buildClassifier(data);
	}

	/**
	 * Returns class probabilities. When minimum expected cost approach is
	 * chosen, returns probability one for class with the minimum expected
	 * misclassification cost. Otherwise it returns the probability distribution
	 * returned by the base classifier.
	 * 
	 * @param instance
	 *            the instance to be classified
	 * @return the computed distribution for the given instance
	 * @throws Exception
	 *             if instance could not be classified successfully
	 */
	public double[] distributionForInstance(Instance instance) throws Exception {

		if (!m_MinimizeExpectedCost) {
			return m_Classifier.distributionForInstance(instance);
		}
		double[] pred = m_Classifier.distributionForInstance(instance);
		double[] costs = m_CostMatrix.expectedCosts(pred, instance);
		/*
		 * for (int i = 0; i < pred.length; i++) { System.out.print(pred[i] +
		 * " "); } System.out.println(); for (int i = 0; i < costs.length; i++)
		 * { System.out.print(costs[i] + " "); } System.out.println("\n");
		 */

		// This is probably not ideal
		int classIndex = Utils.minIndex(costs);
		for (int i = 0; i < pred.length; i++) {
			if (i == classIndex) {
				pred[i] = 1.0;
			} else {
				pred[i] = 0.0;
			}
		}
		return pred;
	}

	/**
	 * Returns the type of graph this classifier represents.
	 * 
	 * @return the type of graph this classifier represents
	 */
	public int graphType() {

		if (m_Classifier instanceof Drawable)
			return ((Drawable) m_Classifier).graphType();
		else
			return Drawable.NOT_DRAWABLE;
	}

	/**
	 * Returns graph describing the classifier (if possible).
	 * 
	 * @return the graph of the classifier in dotty format
	 * @throws Exception
	 *             if the classifier cannot be graphed
	 */
	public String graph() throws Exception {

		if (m_Classifier instanceof Drawable)
			return ((Drawable) m_Classifier).graph();
		else
			throw new Exception("Classifier: " + getClassifierSpec()
					+ " cannot be graphed");
	}

	/**
	 * Output a representation of this classifier
	 * 
	 * @return a string representation of the classifier
	 */
	public String toString() {

		if (m_Classifier == null) {
			return "CostSensitiveClassifier: No model built yet.";
		}

		String result = "CostSensitiveClassifier using ";
		if (m_MinimizeExpectedCost) {
			result += "minimized expected misclasification cost\n";
		} else {
			result += "reweighted training instances\n";
		}
		result += "\n" + getClassifierSpec() + "\n\nClassifier Model\n"
				+ m_Classifier.toString() + "\n\nCost Matrix\n"
				+ m_CostMatrix.toString();

		return result;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.29 $");
	}

	/**
	 * Main method for testing this class.
	 * 
	 * @param argv
	 *            should contain the following arguments: -t training file [-T
	 *            test file] [-c class index]
	 */
	public static void main(String[] argv) {
		runClassifier(new CostSensitiveClassifier(), argv);
	}
}
